1. Mathematics-Informed Neural Network for Acoustic Scattering by Parallel Plates.
- Author
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Sicong Liang and Xun Huang
- Abstract
This paper proposes a mathematics-informed neural network (MINN) approach for resolving the long-term challenge in wave scattering modeling. The central innovation lies in integrating Cauchy-Riemann equations into machine learning architectures. By incorporating Cauchy integrals and boundary conditions, the neural network successfully learns to numerically produce matrix kernel factorization for Wiener-Hopf analytical models. To validate and demonstrate the approach, a benchmark case of wave scattering from parallel hard-soft plates is studied by comparing the machine learning results with the available analytical solutions. The proposed MINN approach could provide a new route to extensively enhance the theoretical modeling capability for several wave scattering and fluid mechanics problems. The code can be found at https://github.com/lscapku/MINN. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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